“The business plans of the next 10,000 startups are easy to forecast: Take X and add AI.” — Kevin Kelly

Here are the breakthrough AI papers and CODE for any industry.

"A hundred years ago electricity transformed countless industries; 20 years ago the internet did, too. Artificial intelligence is about to do the same. To take advantage, companies need to understand what AI can do." — Andrew Ng

If you are a newcomer to the AI, the first question you may have is "What AI can do now and how it relates to my strategies?" Here are the breakthrough AI papers and CODE for any industry.

[0] Bengio, Yoshua, Ian J. Goodfellow, and Aaron Courville. "Deep learning" An MIT Press book. (2016).

"Written by three experts in the field, Deep Learning is the only comprehensive book on the subject." -- Elon Musk, co-chair of OpenAI; co-founder and CEO of Tesla and SpaceX

[1] Richard S. Sutton and Andrew G. Barto. "Reinforcement Learning: An Introduction (2nd Edition)"

[2] Pieter Abbeel and John Schulman | Open AI / Berkeley AI Research Lab. "Deep Reinforcement Learning through Policy Optimization"

[3] Marcin Andrychowicz, Misha Denil, Sergio Gomez, Matthew W. Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, Nando de Freitas. "Learning to learn by gradient descent by gradient descent"

   CODE Learning to Learn in TensorFlow

[0] "Deep Learning Papers Reading Roadmap"

   CODE Download All Papers

[1] CODE "All Code Implementations for NIPS 2016 papers"

    --> "NIPS Spotlight Videos"

[2] arXiv + CODE "Implementations of Some of the Best arXiv Papers"

[0] CODE "Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course."

[1] CODE "Jupyter Notebooks for the Python Data Science Handbook" by Jake Vanderplas.

[2] Video + CODE "Practical Deep Learning For Coders, Part 1" by Jeremy Howard.

[3] arXiv "NIPS 2016 Tutorial: Generative Adversarial Networks" by Ian Goodfellow.

[4] CODE "Data Science Python Notebooks: Deep learning (TensorFlow, Theano, Caffe), Scikit-learn, Kaggle, Big Data (Spark, Hadoop MapReduce, HDFS), Pandas, NumPy, SciPy..."

TensorFlow is an Open Source Software Library for Machine Intelligence: https://www.tensorflow.org

[0] Mart ́ın Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mane ́, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Vie ́gas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. "WhitePaper - TensorFlow: Large-scale machine learning on heterogeneous systems"

   CODE Installation

   CODE TensorFlow Tutorial and Examples for Beginners

   CODE Models built with TensorFlow

The OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms https://gym.openai.com

[1] Greg Brockman and Vicki Cheung and Ludwig Pettersson and Jonas Schneider and John Schulman and Jie Tang and Wojciech Zaremba. "OpenAI Gym WhitePaper"

   CODE Installation of the gym open-source library

   CODE How to create new environments

Universe: A software platform for measuring and training an AI's general intelligence across the world's supply of games, websites and other applications. Universe (blog).

   CODE Installation

   CODE Universe Starter Agent

The following is constructed in accordance with the following three guiding principles:

1. Focus on state-of-the-art;
2. From generic to specific areas; and
3. Clarity, efficiency and transparency.

Being able to deploy with the least possible delay is key.